A redundant manipulator can be defined as a manipulator that has more degrees of freedom than necessary to determine the position and orientation of the end-effector. Such a manipulator has dexterity, flexibility, and the ability to maneuver in the presence of obstacles. This paper presents a solution to the inverse kinematics problem for redundant manipulator based on artificial neural network (ANN). The ANN used is of the supervised type - multilayer feedforward neural network with back error propagation (BEP) training algorithm. The training set for the ANN is obtained by sampling the joint space trajectory of the redundant manipulator arm or from the joint angle encoders of the manipulator. These sampled values of the task space trajectory (end-effector coordinates) are used as the command input vectors to the ANN. By presenting the network with these training set cyclically during training time, the BEP algorithm will change the learning parameters of the ANN so that the sum of the squared difference between the actual joint coordinates and the desired output vectors is minimized.